1. Field of the Invention
This invention generally relates to methods, data structures, and systems for classifying microparticles. Certain embodiments relate to methods for classifying a microparticle using data acquired for the microparticle in combination with a lookup table and one or more algorithms associated with different microparticle classifications.
2. Description of the Related Art
The following descriptions and examples are not admitted to be prior art by virtue of their inclusion within this section.
Generally, flow cytometers provide measurements of fluorescence intensity and other optical properties of microparticles (e.g., laser excited polystyrene beads) as they pass linearly through a flow chamber. A variety of measurements may be performed including, but not limited to, the level of light scattered by the microparticle, the measure of electrical impedance of the microparticle, and one or more measurements of fluorescence of the microparticle. These and any other measurements may be performed by different “channels” of the system (e.g., reporter channels and classification channels), which include a detector and possibly other components (e.g., optical components, electronic components, etc.) coupled to the detector.
Often microparticles may be classified by their one or more of their measurement values, each value corresponding to a different “parameter” (examples of which are noted above) of the microparticle. For example, one common method of classifying microparticles is to graph measurement values in a classification space (e.g., a bitmap) and determine if the graphed location is positioned within a predetermined area of the classification space that corresponds to a particular classification of microparticles. Such a process is referred to herein as a bitmap-based conventional classification method. Unfortunately, the process has its drawbacks. In particular, graphical representation of the classification schemes using this methodology is not easily extended to more than two parameters.
One specific problem encountered in extending the aforementioned classification method to more than two parameters is that the size of the graph scales linearly with the resolution of each parameter used to classify the microparticle, and exponentially according to the number of parameters. For example, if a two-dimensional bitmap has a combined size of 100 units (i.e., 10 units×10 units), then a three-dimensional bitmap will have a combined size of 1,000 units and a four-dimensional bitmap will have a combined size of 10,000 units. Such exponential increases, in some cases, may be completely prohibitive for some system memory capacity. Also, it is noted that parameters of data acquired for microparticles by flow cytometry often have a combined size that is much higher than 100 possible units and typically include three or more parameters. Furthermore, creating bitmaps in more than two dimensions is much more difficult than in two dimensions, since representing a “more than two”-dimensional bitmap in a two-dimensional structure such as a piece of paper or a computer display requires some sacrifice in fidelity to the actual data.
Accordingly, it would be desirable to develop methods, data structures, and systems for classifying particles that can be easily extended beyond more than two parameters, that do not expand memory usage exponentially with each additional parameter, and are structured to minimize the processing time in which particle classification is performed.